pdex

Parallel differential expression for single-cell perturbation sequencing.

Installation

# add to pyproject.toml
uv add pdex

# add to env
uv pip install pdex

Overview

pdex computes per-gene differential expression statistics between perturbation groups in single-cell data using Mann-Whitney U tests with FDR correction. It was originally designed for CRISPR screen and perturbation sequencing datasets with many groups and large cell counts.

It supports dense and sparse (CSR) expression matrices, and uses numba-mwu for Numba-accelerated Mann-Whitney U computation.

Modes

Usage

Reference mode (default)

import anndata as ad
from pdex import pdex

adata = ad.read_h5ad("screen.h5ad")

results = pdex(
    adata,
    groupby="guide",
    mode="ref",
    is_log1p=False,
)

1-vs-rest mode

results = pdex(
    adata,
    groupby="guide",
    mode="all",
    is_log1p=False,
)

On-target mode

Requires a column in adata.obs mapping each group to its target gene:

results = pdex(
    adata,
    groupby="guide",
    mode="on_target",
    gene_col="target_gene",
    is_log1p=False,
)

Parameters

CPM filter

cpm_filter is an opt-in floor filter. When set to a threshold T, a (target, feature) row is dropped only when the gene's pooled CPM is <= T in both the target group and the reference group. Rows are kept when either side has CPM > T.

The CPM view is used only for filtering: reported means, fold changes, MWU statistics, and p-values are still computed from the original expression scale. When is_log1p=True, counts are recovered with expm1 before CPM is computed. FDR is corrected over the surviving genes only.

Output

Returns a Polars DataFrame (or pandas if as_pandas=True) with one row per (group, gene) pair: